In this chapter, you learned how to gather, organize, and parse data. This is the first step, and one of the most important step, in developing machine learning models, but having data does not get us very far if we do not gain some intuition about our data and put it into a standard form for processing. Next, we will tackle some techniques for further structuring our data (matrices) and for understanding our data (statistics and probability).
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Machine Learning With Go
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Machine Learning With Go
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Overview of this book
The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios.
Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization.
The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages.
Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.
Table of Contents (11 chapters)
Preface
Free Chapter
Gathering and Organizing Data
Matrices, Probability, and Statistics
Evaluation and Validation
Regression
Classification
Clustering
Time Series and Anomaly Detection
Neural Networks and Deep Learning
Deploying and Distributing Analyses and Models
Algorithms/Techniques Related to Machine Learning
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